Abstract: Systems and methods for instantiating a filter for a process graph are provided. A process graph of a workflow is received. Context data associated with the process graph is stored. A filter is instantiated to filter the process graph based on the stored context data. The filtered process graph is output.

Abstract: Systems and methods for configuring an RPA (robotic process automation) platform to perform a candidate process automation are provided. Discovery data relating to a candidate process automation is generated. RPA platform design components for configuring an RPA platform to perform the candidate process automation are generated based on the discovery data. The RPA platform is configured based on the RPA platform design components.

Abstract: Automation windows for robotic process automation (RPA) using multiple desktops are disclosed. One or more robot desktops are launched, and one or more RPA robots operate in the robot desktop(s). The robot desktops may not initially be the active desktop. When the robot(s) execute their automations, if an activity in the RPA robot workflow needs the robot desktop to be the active desktop, the active desktop is switched to the appropriate robot desktop when the appropriate robot desktop is not already active, and activit(ies) of the RPA workflow that require the robot desktop to be the active desktop are executed. In some embodiments, after RPA workflow execution finishes, the robot desktop, the RPA robot, or both, are automatically closed.

Abstract: To performing real-time bidirectional communication between robotic process automation (RPA) robots and a web application, a robot initiates execution of a primary workflow when a request is received from a web application. The robot then listens for one or more triggers from the web application to start execution of a secondary workflow, and executes the secondary workflow when the one or more triggers are detected. The robot also returns a result of the execution of the secondary workflow to the web application.

Abstract: A digital assistant may execute one or more tasks using robotic processing automation (RPA). The digital assistant (or robot) assigns a workflow to a robot to monitor for one or more triggers. The one or more triggers comprise one or more events causing a robot to perform an automated tasks with or without user involvement. The robot also identifies the one or more triggers during the monitoring of the one or more triggers, and loads a workflow associated with the one or more identified triggers. The robot further includes executing the loaded workflow to perform one or more tasks associated with the one or more triggers.

Abstract: An artificial intelligence (AI)/machine learning (ML) recommendation engine for robotic process automation (RPA) is disclosed. An AI/ML model may be trained to provide recommendations for a next activity, a next sequence of activities, and/or modifications to parameters for one or more existing activities to include during RPA workflow development. The recommendations may be based on the context of where the user is in the RPA workflow. For user interface (UI) automations, the AI/ML model may be linked to an object repository and trained to make recommendations therefrom. The AI/ML model may also be trained to generate new UI descriptors for the object repository.

Abstract: Training and using artificial intelligence (AI)/machine learning (ML) models to automatically supplement and/or complete code of RPA workflows is disclosed. A trained AI/ML model may intelligently and automatically predict and complete the next series of activities in RPA workflows (e.g., one, a few, many, the remainder of the workflow, etc.). Actions users take while creating workflows over a time period may be captured and stored. The AI/ML model may then be trained and used to match the stored actions with stored workflow sequences of actions in order to predict and complete the workflow. As more and more workflow sequences are captured and stored over time, the AI/ML model may be retrained to predict a larger number of sequences and/or to more accurately make predictions. Auto-completion may occur in real-time in some embodiments to save time and effort by the user.

Abstract: Systems and methods for implementing robotic process automation (RPA) in the cloud are provided. An instruction for managing an RPA robot is received at an orchestrator in a cloud computing environment from a user in a local computing environment. In response to receiving the instruction, the instruction for managing the RPA robot is effectuated.

Abstract: Systems and methods for determining conformance of a process based on a process model of the process and an event log of an execution of the process are provided. The process model is divided into one or more control regions and reachable nodes are determined for each node in the process model. Conformance of the process is determined by comparing transitions from source activities to destination activities in the event log with the reachable nodes based on the one or more control regions.

Abstract: Systems and methods for executing a robotic process automation (RPA) workflow are provided. The RPA workflow is executed by a first robot. The execution of the RPA workflow is suspended by the first robot. A current context of the RPA workflow is serialized at a time of the suspension and the current context of the RPA workflow is stored. The execution of the RPA workflow is resumed by a second robot based on a triggering condition by retrieving the current context of the RPA workflow. The first robot and the second robot may be the same robot or different robots.

Abstract: A process for invoking a robot from an application may include launching the application from a computing system to invoke a robot link embedded within the application. The process may also include initiating from the application a port discovery process to identify a port, port details, and a token. The process may further include generating by the application a randomized code and invoking a consent application requesting approval from a user of the computing system to invoke the robot from the application. The process may also include registering the randomized code with a local listener module and passing user information and the token to a global listener module. The process may further include receiving from the global listener module the token and port identification, allowing the application to authenticate itself with, and communicate with, the robot, thereby completing the robot invoking process.

Abstract: A digital assistant may execute one or more tasks using robotic processing automation (RPA). The digital assistant (or robot) assigns a workflow to a robot to monitor for one or more triggers. The one or more triggers comprise one or more events causing a robot to perform an automated tasks with or without user involvement. The robot also identifies the one or more triggers during the monitoring of the one or more triggers, and loads a workflow associated with the one or more identified triggers. The robot further includes executing the loaded workflow to perform one or more tasks associated with the one or more triggers.

Abstract: Disclosed herein is a computing system. The computing system includes a memory and a processor. The memory stores processor executable instructions for a workflow recommendation assistant engine. The processor is coupled to the memory. The processor executes the workflow recommendation assistant engine to cause the computing device to analyze images of a user interface corresponding to user activity, execute a pattern matching of the images with respect to existing automations, and provide a prompt indicating that an existing automation matches the user activity.

Abstract: A method is provided. The method is executed by a modeling engine implemented as a computer program within a computing environment. The method includes translating a pre-processed model based on an unstructured existing process into a patched process tree. The translating includes detecting, structuring, and translating rigids of the pre-processed model into corresponding process tree equivalent elements of the patched process tree.

Abstract: Some embodiments improve the autonomy of a form-filling RPA robot by supplying it with a strategy ranking indicator identifying multiple alternative strategies for filling in a target form field, so that when one strategy fails, the RPA robot may fall back on an alternative strategy. The ranking indicator further ranks the respective form-filling strategies, enabling the RPA robot to choose among the available strategies according to their rank. Some embodiments prioritize higher-ranking strategies. The strategy ranking indicator is dynamically updated to promote successful form-filling strategies and/or demote failing ones, and is made available to other instances of the RPA robot.

Abstract: The present system and method relate generally to the field of Robotic Process Automation, particularly to a form data extractor for document processing. The system and method relate to a form extractor for document processing using RPA workflows that can be easily configured for different document types. The form extractor includes a set of templates for identifying the document type (classification) and extracting data from the documents. The templates can be configured, i.e., by the user, by defining the fields to be extracted and the position of the field on the document. The form extractor is resilient to changes in the position of the template on a page, as well as to scan rotation, size, quality, skew angle variations and file formats, thus allowing RPA processes to extract data from documents that need ingestion, independent of how they are created.

Abstract: Human-in-the-loop robot training using artificial intelligence (AI) for robotic process automation (RPA) is disclosed. This may be accomplished by a listener robot watching interactions of a user or another robot with a computing system. Based on the interactions by the user or robot with the computing system, the robot may be improved and/or personalized for the user or a group of users.

Abstract: Security automation, such as penetration testing or security hardening, is performed using robotic process automation (RPA) by directly connecting one or more robots into an operating system of a platform. The one or more robots execute a workflow to simulate the penetration testing of the operating system to identify malicious activity or vulnerable configurations within the operating system. The one or more robots also generate a report for the user identifying the malicious activity, misconfigurations or vulnerabilities within the environment.

Abstract: Target-based schema identification and semantic mapping for robotic process automation (RPA) are disclosed. When looking at a source, such as a document, a web form, a user interface of a software application, a data file, etc., it is often difficult for software to determine which fields are labels and which are values associated with those labels. Since values have not yet been entered for various labels (e.g., first name, company, customer number, etc.), these labels are easier to detect than when the target also includes various values associated with the labels. A selection of an empty target may be received and target-based schema identification may be performed on the empty target, determining labels and a type of the target. Semantic matching may then be performed between a source and the target. These features may be performed at design time or runtime.

Abstract: Disclosed herein is a computing device that includes a memory and a processor, which is coupled to the memory. The memory stores processor executable instructions for a robotic process engine. In operation, the robotic process engine generates a robot tray comprising a canvas and dynamically configures the canvas based on inputs. The dynamic configuring includes adding a widget onto the canvas.